Development of a novel extension of Rayleigh distribution with application to COVID-19 data
Abstract Effective analysis of medical data is essential for understanding complex healthcare phenomena. Probability distribution models offer a structured approach to uncover patterns in such data, particularly for studying disease progression, survival analysis and many more. In this study, we exp...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-03645-w |
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| author | Danish Qayoom Aafaq A. Rather Ohud A. Alqasem Zahoor Ahmad M. Nagy Abdirashid M. Yousuf A. H. Mansi Eslam Hussam Ahmed M. Gemeay |
| author_facet | Danish Qayoom Aafaq A. Rather Ohud A. Alqasem Zahoor Ahmad M. Nagy Abdirashid M. Yousuf A. H. Mansi Eslam Hussam Ahmed M. Gemeay |
| author_sort | Danish Qayoom |
| collection | DOAJ |
| description | Abstract Effective analysis of medical data is essential for understanding complex healthcare phenomena. Probability distribution models offer a structured approach to uncover patterns in such data, particularly for studying disease progression, survival analysis and many more. In this study, we explore a novel probability distribution model, derived by applying the DUS transformation to the standard Rayleigh distribution. We thoroughly investigate the statistical properties of the proposed model and derive key reliability measures to demonstrate its applicability in reliability analysis. To ensure precise parameter estimation, various estimation methods are evaluated, and their effectiveness is assessed through a detailed simulation study using bias, mean squared error, and mean relative error as performance criteria. The developed model’s practical applicability is demonstrated with an analysis of COVID-19 data, comparing its performance with several well-known distributions. The results highlight the flexibility and accuracy of the model, establishing it as a powerful and reliable tool for advanced statistical modelling in healthcare research. |
| format | Article |
| id | doaj-art-94e514225b774d1fbffa8a2be08a1eba |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-94e514225b774d1fbffa8a2be08a1eba2025-08-20T03:16:51ZengNature PortfolioScientific Reports2045-23222025-05-0115112810.1038/s41598-025-03645-wDevelopment of a novel extension of Rayleigh distribution with application to COVID-19 dataDanish Qayoom0Aafaq A. Rather1Ohud A. Alqasem2Zahoor Ahmad3M. Nagy4Abdirashid M. Yousuf5A. H. Mansi6Eslam Hussam7Ahmed M. Gemeay8 Symbiosis Statistical Institute, Symbiosis International (Deemed University) Symbiosis Statistical Institute, Symbiosis International (Deemed University)Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman UniversityDepartment of Statistics, University of KashmirDepartment of Statistics and Operations Research, College of Science, King Saud UniversityResearch and Innovation Center, Amoud UniversityDICA, Politecnico di MilanoDepartment of Accounting, College of Business Administration in Hawtat Bani Tamim, Prince Sattam Bin Abdulaziz UniversityDepartment of Mathematics, Faculty of Science, Tanta UniversityAbstract Effective analysis of medical data is essential for understanding complex healthcare phenomena. Probability distribution models offer a structured approach to uncover patterns in such data, particularly for studying disease progression, survival analysis and many more. In this study, we explore a novel probability distribution model, derived by applying the DUS transformation to the standard Rayleigh distribution. We thoroughly investigate the statistical properties of the proposed model and derive key reliability measures to demonstrate its applicability in reliability analysis. To ensure precise parameter estimation, various estimation methods are evaluated, and their effectiveness is assessed through a detailed simulation study using bias, mean squared error, and mean relative error as performance criteria. The developed model’s practical applicability is demonstrated with an analysis of COVID-19 data, comparing its performance with several well-known distributions. The results highlight the flexibility and accuracy of the model, establishing it as a powerful and reliable tool for advanced statistical modelling in healthcare research.https://doi.org/10.1038/s41598-025-03645-w |
| spellingShingle | Danish Qayoom Aafaq A. Rather Ohud A. Alqasem Zahoor Ahmad M. Nagy Abdirashid M. Yousuf A. H. Mansi Eslam Hussam Ahmed M. Gemeay Development of a novel extension of Rayleigh distribution with application to COVID-19 data Scientific Reports |
| title | Development of a novel extension of Rayleigh distribution with application to COVID-19 data |
| title_full | Development of a novel extension of Rayleigh distribution with application to COVID-19 data |
| title_fullStr | Development of a novel extension of Rayleigh distribution with application to COVID-19 data |
| title_full_unstemmed | Development of a novel extension of Rayleigh distribution with application to COVID-19 data |
| title_short | Development of a novel extension of Rayleigh distribution with application to COVID-19 data |
| title_sort | development of a novel extension of rayleigh distribution with application to covid 19 data |
| url | https://doi.org/10.1038/s41598-025-03645-w |
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